Simulation Modeling of Intelligent Control Algorithms for Constructing Autonomous Power Supply Systems with Improved Energy Efficiency

Detalles Bibliográficos
Parent link:MATEC Web of Conferences
Vol. 155 : Information and Measuring Equipment and Technologies (IME&T 2017).— 2018.— [01032, 7 p.]
Autor principal: Gimazov R. Ruslan
Autor Corporativo: Национальный исследовательский Томский политехнический университет Инженерная школа энергетики Научно-образовательный центр И. Н. Бутакова (НОЦ И. Н. Бутакова)
Otros Autores: Shidlovskiy S. V. Stanislav Viktorovich
Sumario:Title screen
The paper considers the issue of supplying autonomous robots by solar batteries. Low efficiency of modern solar batteries is a critical issue for the whole industry of renewable energy. The urgency of solving the problem of improved energy efficiency of solar batteries for supplying the robotic system is linked with the task of maximizing autonomous operation time. Several methods to improve the energy efficiency of solar batteries exist. The use of MPPT charge controller is one these methods. MPPT technology allows increasing the power generated by the solar battery by 15 - 30%. The most common MPPT algorithm is the perturbation and observation algorithm. This algorithm has several disadvantages, such as power fluctuation and the fixed time of the maximum power point tracking. These problems can be solved by using a sufficiently accurate predictive and adaptive algorithm. In order to improve the efficiency of solar batteries, autonomous power supply system was developed, which included an intelligent MPPT charge controller with the fuzzy logic-based perturbation and observation algorithm. To study the implementation of the fuzzy logic apparatus in the MPPT algorithm, in Matlab/Simulink environment, we developed a simulation model of the system, including solar battery, MPPT controller, accumulator and load. Results of the simulation modeling established that the use of MPPT technology had increased energy production by 23%; introduction of the fuzzy logic algorithm to MPPT controller had greatly increased the speed of the maximum power point tracking and neutralized the voltage fluctuations, which in turn reduced the power underproduction by 2%.
Lenguaje:inglés
Publicado: 2018
Materias:
Acceso en línea:https://doi.org/10.1051/matecconf/201815501032
http://earchive.tpu.ru/handle/11683/46992
Formato: Electrónico Capítulo de libro
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=657821